Summary of Grounding Is All You Need? Dual Temporal Grounding For Video Dialog, by You Qin et al.
Grounding is All You Need? Dual Temporal Grounding for Video Dialog
by You Qin, Wei Ji, Xinze Lan, Hao Fei, Xun Yang, Dan Guo, Roger Zimmermann, Lizi Liao
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Dual Temporal Grounding-enhanced Video Dialog model (DTGVD) is a novel approach to video dialog response generation that merges the strengths of two dominant methods. By emphasizing dual temporal relationships, DTGVD predicts dialog turn-specific temporal regions and filters video content accordingly. It also grounds responses in both video and dialog contexts, recognizing chronological interplay between different dialog turns. To further align video and dialog temporal dynamics, a list-wise contrastive learning strategy is implemented. Evaluations using the AVSD@DSTC-7 and AVSD@DSTC-8 datasets demonstrate the superiority of this methodology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new model called DTGVD that helps computers understand videos and conversations better. It’s like having a conversation with someone who watches TV or a movie with you. The model looks at the video and what people are saying, and it tries to predict what will be said next. It does this by looking at how things change over time – like when someone is talking about something that happened earlier in the conversation. The model also uses something called “contrastive learning” to make sure its predictions are accurate. This means it’s trained on lots of examples and learns to tell the difference between good and bad predictions. Overall, this new model can understand videos and conversations better than other models, and it could be useful for things like chatbots or language translation. |
Keywords
» Artificial intelligence » Grounding » Translation
